D-RISK: Validating the autonomous vehicle safety-case using simulation and adaptive test generation

“** 需要 **仿真将成为互联自动驾驶汽车（CAV）安全案例的重要组成部分，从而实现自动驾驶汽车控制系统（CAV）的验证和认证。这在城市中至关重要，因为其他主体的密度和多样性，包括道路使用者，行人，基础设施和标志，以及交通方式的连通性，呈现出无限的潜在场景，必须安全地进行协商，没有一个现有的模拟系统能够在一个总体风险架构内实现一个完整的安全案例认证方法，确定关键绩效指标和领域。现有物理模拟器和模型的局限性无法捕捉到：* 在这些背景下，边缘情况场景的高度复杂性，难以预测，与大多数日常驾驶场景相比，发生频率较低 * 其他道路使用者和行人对CAV的不可预测的反应 * 快速变化的技术和监管环境 * 相关的社会经济和人为因素从根本上讲，对于一种连续的真实世界模拟和相应的模拟平台来说，存在太多的风险类型和水平。破坏性解决方案。**作为回应，aiPod汇集了一个雄心勃勃的跨行业联盟，包括帝国理工学院伦敦，Claytex，DG城市和运输伦敦，开发和集成，作为模拟器系统的一部分，一个新的平台无关的场景生成器，用于模拟测试SAE 4/5级城市自治在单个传感器的规模，CAV和整个运输网络。适用于所有潜在的CAV变体和任何提供安全案例方法的仿真架构，该解决方案的目标是能力的阶跃变化：1.识别和结构复杂的现实边缘情况下的业务2.自适应地制定最相关的测试例程，以虚拟验证CAV决策性能3。定性和量化总体风险，在相关场景和参数空间中聚合多层次框架的目标是前所未有的能力，从多模式真实的和模拟数据中识别复杂的边缘案例测试场景，人为故障检测和对CAV的公共响应。克服现有模拟器和模型的局限性，该项目的目标是一个功能架构，它本质上是可进化和可扩展的，同时允许对测试和认证特定部署的约束。该解决方案和更广泛的项目提供的可操作的反馈可以用来发展整个英国的自动驾驶汽车仿真能力和监管地位。"

"**Need.**Simulation will be a vital component in the Connected Autonomous Vehicle (CAV) safety-case, enabling the validation and certification of autonomous Vehicle Control Systems (VCS). This is essential in cities where the density and diversity of other agents, including road-users, pedestrians, infrastructure and signage, as well as intermodal-connectivity, present a limitless set of potential scenarios that must be safely negotiated, either individually or collectively.No existing simulation system enables a full safety-case approach to certification within an overarching risk architecture based on clearly-defined key performance indicators and areas. Limitations on incumbent physical simulators and models fail to capture:* High complexity of edge-case scenarios in these contexts, which are difficult to predict and occur with low frequency compared to the majority of everyday driving scenarios* Unpredictable responses of other road users and pedestrians to CAVs* Rapidly changing technology and regulatory landscapes* Relevant socioeconomic and human factorsFundamentally, too many individual types and levels of risk exist for just one kind of continuous real-world simulation and corresponding simulation platform.**Disruptive solution.**In response, aiPod have brought together an ambitious cross-industry consortium, including Imperial College London, Claytex, DG Cities and Transport for London, to develop and integrate, as part of a simulator system, a novel platform-agnostic scenario generator for simulated testing of SAE Level-4/5 urban autonomy at the scale of the individual sensor, CAV and entire transport network.Applicable to all potential CAV variants and any simulation architecture feeding a safety-case approach, the solution targets a step change in capability to:1. Identify and structure complex realistic edge-case scenarios2. Adaptively formulate the most relevant test routines to virtually-validate CAV decision-making performance3. Qualify and quantify overall risk, aggregated across the relevant scenarios and parameter spaceA multi-level framework targets unprecedented capability to identify complex edge-case test scenarios from multi-mode real and simulated data, human-driven fault-detection and public responses to CAV.Overcoming limitations of existing simulators and models, the project targets a functional architecture that is inherently evolvable and extendable, whilst allowing constraints to test and certify specific deployments. Actionable feedback delivered by the solution and wider project can be leveraged to evolve the overall UK autonomous vehicle simulation capability and regulatory position."